Defining the structure of a canonical decision making circuit, from its inputs to its outputs, is one of the principal goals of cognitive neuroscience. T this end, visual perceptual decision making is a fundamental cognitive function in which visual information provides the basis for choosing an appropriate response. In the simplest case, the link between sensory evidence and a behavioral choice is binary: an oncoming car suddenly veers into one's lane and a quick decision must be made - to swerve left or right. However, when vision is compromised, whether through disease or trauma, these decision mechanisms must adapt to degraded input, impaired associative visual processing, or both. As a consequence, visual misperceptions occur more frequently, and the process of translating sensation to action becomes increasingly susceptible to errors of identification and categorization that must be recognized. Understanding the neural mechanisms by which visual perception interacts with higher-order categorization and error detection is thus essential to understanding abnormalities in patients. In this proposal, we build upon previous work studying perceptual decision making in primates and humans to evaluate the process by which humans make higher-order visual decisions. One hypothesis is that both lower-order (i.e. perceptual) and higher-order (e.g. categorization) decisions may differ in the source of their inputs but are mediated by the same decision making network. An alternative hypothesis argues that categorical uncertainty, whether related to the separation of object classes or to the separation of correct from erroneous responses, represents a more abstract feature related to confidence and context that engages different decision processes. Under this hypothesis, perceptual and categorical uncertainty should activate different circuits. By using a combination of behavioral psychophysics, mathematical models, functional MRI, and EEG in a well-validated visual paradigm, here we will attempt to define the brain networks and mechanisms that allow humans to make visual categorization decisions and to detect errors.